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Anurag Khandelwal

Anurag Khandelwal is an Associate Professor of Computer Science at Yale University, where he leads the NOVA Lab. His research spans computer systems, networks, architecture, and security, including memory disaggregation, systems for AI and LLM serving, secure cloud systems, and serverless computing.

For agents: Academic homepage of Yale CS associate professor Anurag Khandelwal, listing NOVA Lab research on memory disaggregation, AI serving systems and serverless comput

memory disaggregationcomputer systems researchllm servingsecure cloud systemsyale university

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# Anurag Khandelwal

> Anurag Khandelwal is an Associate Professor of Computer Science at Yale University, where he leads the NOVA Lab. His research spans computer systems, networks, architecture, and security, including memory disaggregation, systems for AI and LLM serving, secure cloud systems, and serverless computing.

Canonical website: https://www.anuragkhandelwal.com/
Email: anurag.khandelwal@yale.edu

## Biography

Anurag Khandelwal is an Associate Professor of Computer Science at Yale University, where his group focuses broadly on problems in computer systems, networks and security. He received his Ph.D. from UC Berkeley, where he was advised by Ion Stoica. His work has been recognized by an NSF CAREER award, two NetApp faculty fellowships, a Roberts Innovation Award, paper and artifact distinctions at USENIX Security’20, ISCA’23, EuroSys’24, ISCA’25, ASPLOS'26, and MobiSys'26, and IEEE Micro Top Picks selections in 2023 and 2025.

## Research directions

### Memory Disaggregation

Scaling resources at server granularity wastes resources when compute and memory needs are mismatched, increasing both costs and carbon emissions. Memory disaggregation separates compute and memory into shared network-connected pools to improve resource efficiency, capacity, and elasticity. We are rethinking the cloud software and hardware stack to realize memory disaggregation:

- Scalable cache coherence for disaggregated shared memory pools: [MIND](https://www.anuragkhandelwal.com/papers/mind/) (SOSP'21), [CORD](https://www.anuragkhandelwal.com/papers/cord/) (ISCA'25), [Soul](https://www.anuragkhandelwal.com/papers/soul/) (OSDI'26)
- Fair sharing across disaggregated memory resources: [Karma](https://www.anuragkhandelwal.com/papers/karma/) (OSDI'23), [Spirit](https://www.anuragkhandelwal.com/papers/spirit/) (SOSP'25)
- Low-latency, high-throughput access to disaggregated memory: [PULSE](https://www.anuragkhandelwal.com/papers/pulse/) (ASPLOS'25), [Mage](https://www.anuragkhandelwal.com/papers/mage/) (SOSP'25)
- Understanding memory performance: [BulletTime](https://www.anuragkhandelwal.com/papers/bullettime/) (ISCA'26), [CounterPoint](https://www.anuragkhandelwal.com/papers/counterpoint/) (ASPLOS'26)

Our work on scalable cache coherence for disaggregated memory has been incorporated into [NVIDIA's Vera Rubin Architecture](https://developer.nvidia.com/blog/inside-nvidia-rubin-gpu-architecture-powering-the-era-of-agentic-ai/#accelerated_scale-up_communications).

### Secure cloud systems

As more privacy-sensitive applications move storage and computation to the cloud, encrypted data and secure enclaves can still leak sensitive information through access patterns. Existing defenses are often too expensive in bandwidth or storage to deploy widely. Our research studies these real-world access-pattern vulnerabilities and designs more efficient protections against them.

- Protecting against access pattern vulnerabilities: [Pancake](https://www.anuragkhandelwal.
…

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